Proceedings of the 2nd International Conference on Information Economy, Data Modeling and Cloud Computing, ICIDC 2023, June 2–4, 2023, Nanchang, China

Research Article

Research on Solving Hamiltonian Loop of Material Distribution Based on Ant Colony Algorithm

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  • @INPROCEEDINGS{10.4108/eai.2-6-2023.2334611,
        author={Lixuan  Liu and Zhixian  Kong and Junrong  Ma and Yaying  Deng and Liangchuan  Ma and Xingding  Wu},
        title={Research on Solving Hamiltonian Loop of Material Distribution Based on Ant Colony Algorithm},
        proceedings={Proceedings of the 2nd International Conference on Information Economy, Data Modeling and Cloud Computing, ICIDC 2023, June 2--4, 2023, Nanchang, China},
        publisher={EAI},
        proceedings_a={ICIDC},
        year={2023},
        month={8},
        keywords={dijkstra algorithm genetic algorithm tsp problem},
        doi={10.4108/eai.2-6-2023.2334611}
    }
    
  • Lixuan Liu
    Zhixian Kong
    Junrong Ma
    Yaying Deng
    Liangchuan Ma
    Xingding Wu
    Year: 2023
    Research on Solving Hamiltonian Loop of Material Distribution Based on Ant Colony Algorithm
    ICIDC
    EAI
    DOI: 10.4108/eai.2-6-2023.2334611
Lixuan Liu1,*, Zhixian Kong1, Junrong Ma1, Yaying Deng1, Liangchuan Ma1, Xingding Wu1
  • 1: Space Engineering University
*Contact email: 652813493@qq.com

Abstract

With the increasing popularity of 5G communication technology, the high speed and low latency of 5G network provide information technology support for the development of the UAV industry. Nowadays, the material distribution mode of "delivery vehicle + drone" is widely used in many tasks such as disaster relief and cargo transportation. At present, in order to improve the transportation efficiency and reduce operating costs in this mode, it is particularly important to reasonably plan the distribution scheme of drones and vehicles. Based on the given location, route and distribution demand, the optimal distribution scheme is solved, the Dijkstra algorithm is used to calculate the shortest distance between any two points in the network diagram and the corresponding path to build a fully connected network, and then the genetic algorithm is used to solve the TSP problem with the shortest distance of the vehicle as the objective function, so as to approximate a Hamilton loop, and finally find the actual driving path of the vehicle.